Global motion invariant signatures for fast and accurate motion tracking in a digital image-based elasto-tomography system

ABSTRACT

A method for converting digital images of an actuated breast into an accurate description of breast surface motion from a digital image-based elasto-tomography system comprises the steps of artificially placing a high density of fiducial markers on the breast surface, whereby the fiducial markers have different qualities and are placed in different proportions according to their quality; utilizing motion invariant properties of the fiducial markers to form a global motion invariant signature; tracking the markers on the actuated breast surface from image to image in each digital camera using the global motion invariant signature; and using the cameras calibration to measure the breast surface motion.

FIELD OF THE INVENTION

The invention relates generally to the field of breast cancer screening,and in particular to a technique of breast surface motion tracking in adigital image-based elasto-tomography system.

BACKGROUND OF THE INVENTION

Breast cancer is a significant health problem in both developed anddeveloping countries. It is estimated that each year the disease isdiagnosed in over 1,000,000 women worldwide and is the cause of death inover 400,000 women. There are many treatment options available,including surgery, chemotherapy, radiation therapy, and hormonaltherapy. These treatments are significantly more effective in reducingthe mortality of the disease with early detection through breast cancerscreening programmes.

The standard method for detection of breast cancer is mammography.However mammography can cause significant patient discomfort andrequires radiation exposure. Furthermore there are often variableresults and inconsistencies in reading and interpreting the images ofbreast tissue from the X-Ray machine especially for smaller tumour sizesof the order of 1-5 mm.

Digital Image based Elasto-tomography is an emerging technology fornon-invasive breast cancer screening without the requirement ofradiation. As used herein, Digital Image-based Elasto-Tomography systemwill be referred to as a DIET system. The DIE™ system uses digitalimaging of an actuated breast surface to determine tissue surfacemotion. It then reconstructs the three-dimensional internal tissuestiffness distribution from that motion. Regions of high stiffnesssuggest cancer since cancerous tissue is between 3 and 10 times stifferthan healthy tissue in the breast. This approach eliminates the need forX-Rays and excessive, potentially painful compression of the breast asrequired in a mammogram. Hence, screening could start much younger andenjoy greater compliance. Presently, there are other elasto-tomographicmethods based on magnetic resonance and ultrasound modalities. Bothmethods are capable of measuring the tissue elasticity and they areundergoing rapid development across the globe. However, they are alsocostly, in terms of equipment, and take significant time to use. Theyare therefore limited for practical screening applications.

The DIET system, in contrast, is silicon based and is thus potentiallylow cost and portable, so the technology could be used in any medicalcentre, particularly in remote areas. In addition, the use of silicontechnology ensures that as it improves and scales upward in capabilityso will the DIET system performance. This scalability of performance isnot true for X-Ray or ultrasound based approaches.

The DIET system relies on a fast and accurate measurement of theactuated breast using multiple calibrated high-resolution digitalcameras. Furthermore small perturbations and variations on the surfacemust be measured accurately to ensure smaller tumours are not missed.Therefore, there exists a need in the art for very high-resolutionfeature registration and motion tracking system that can deal with theunique requirements of a DIET system. In addition, for clinicaleffectiveness, the measured motion must be done with a minimal amountcomputation.

SUMMARY OF THE INVENTION

The present invention is directed towards overcoming the problem of veryhigh resolution feature registration and motion tracking with a minimalamount of computation in connection with the DIET system; consisting ofa patient bed, an actuator to induce oscillation in the breast, an arrayof digital cameras and computer software for processing images of thebreast surface and transforming into measured motion, and computersoftware for converting measured motion into a three-dimensionaldistribution of stiffness of the breast.

Briefly summarized, according to one aspect of the invention a methodfor converting digital images of an actuated breast into an accuratedescription of breast surface motion from such a DIET system asdescribed above comprises the steps of artificially placing a highdensity of fiducial markers on the breast surface, whereby the fiducialmarkers have different qualities and are placed in different proportionsaccording to their quality; utilizing motion invariant properties of thefiducial markers to form a global motion invariant signature; trackingthe markers on the actuated breast surface from image to image in eachdigital camera using the global motion invariant signature; and usingthe cameras calibration to measure the breast surface motion.

In one form, the invention is a method for generating a high resolutionfeature registration and motion tracking system with minimal computationwherein the method comprising the steps of artificially placing aplurality of fiducial markers on a surface of a breast, each of themarkers having a characteristic according to a class of characteristics,the class having a plurality of subclasses wherein the markers in eachsubclass have a common characteristic, the subclasses each having aunique number of markers; tracking the motion of the markers of a firstsubclass having the fewest number of markers on the surface from a firstimage to a second image; partitioning the surface based upon the firstsubclass of markers; tracking the motion of the markers of a secondsubclass with the next fewest number of markers within each partition;and partitioning the surface based upon the second subclass of markers.

In another form, the invention is a method for generating a highresolution feature registration and motion tracking system with minimalcomputation in connection with a digital image-based elasto-tomographysystem, the method comprising the steps of: artificially placing aplurality of fiducial markers on a tissue surface; actuating the tissuesurface; imaging the tissue surface with an array of digital cameras;choosing motion invariant properties of the fiducial markers to form aglobal motion invariant signature; tracking the markers on the actuatedtissue surface from image to image in each digital camera using theglobal motion invariant signature; and using the tracked motion in eachcamera and the camera calibration to measure tissue surface motion.

In another form, the invention is a method for generating a highresolution feature registration and motion tracking system with minimalcomputation in connection with a digital image-based elasto-tomographysystem the method comprising the steps of: artificially placing aplurality of fiducial markers on a tissue surface; imaging the tissuesurface with an array of spatially calibrated digital cameras; choosingcamera invariant properties of the fiducial markers and forming cameraangle invariant signatures; identifying common markers in the images ofa non-actuated tissue between all cameras in the array using the cameraangle invariant signatures; tracking the markers on the actuated tissuesurface from image to image in each digital camera using a global motioninvariant signature; using the tracked motion in each camera and thecamera calibration to measure tissue surface motion.

In another form, the invention is a digital image-basedelasto-tomography apparatus, comprising: an array of spatiallycalibrated cameras; a vibration unit situated proximate to the array ofcameras; and a computer system in electrical communication with thecameras and configured for computing the surface motion of an objectactuated by the vibration unit and within a field of view of thecameras.

The invention has the advantages of accurately and efficiently trackinglarge numbers of markers that are close together for a large array ofdigital cameras. The invention has flexibility in varying the type ofpattern and shape of the fiducial markers, and distribution density ofthe fiducial markers for improved accuracy and allows significantfreedom in the amount of pixel movement between images in featureregistration for improved efficiency.

These and other aspects, objects, features and advantages of the presentinvention will be more clearly understood and appreciated from a reviewof the following description of the preferred embodiment and appendedclaims, and by reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is disclosed with reference to the accompanyingdrawings, wherein:

FIG. 1A is a view of an apparatus implementing the DIET system accordingto the present invention.

FIG. 11B is a diagram summary of the DIET system.

FIG. 2 is a block diagram of a method, according to the presentinvention, of generating the measurement of the breast surface motionunder actuation in a DIET system.

FIG. 3 is a block diagram of a detailed embodiment of the step ofdetermining the motion of the red points as shown in FIG. 2.

FIG. 4 is an artificially simulated group of blue dots in an imagediffering by an induced linear motion in a first example.

FIG. 5 is the linear invariant signature for the example of blue dotsdemonstrating the concept in the first example.

FIG. 6 is the registration of the blue dots between images in the firstexample.

FIG. 7 is an artificially generated binary image containing 1000 circlesaccording to a second example.

FIG. 8 is the induced motion field of the binary image in FIG. 7.

FIG. 9 is a geometric method of ruling out non-corresponding points inthe second example.

FIG. 10 is the registration of blue points between images in the secondexample.

FIG. 11 is two images of deformations of a visco-elastic breast phantomaccording to a third example.

FIG. 12 is the registration of the blue dots between images in the thirdexample.

FIG. 13 is the final motion field of all the points in the thirdexample.

Corresponding reference characters indicate corresponding partsthroughout the several views. The examples set out herein illustrateseveral embodiments of the invention but should not be construed aslimiting the scope of the invention in any manner.

DETAILED DESCRIPTION

There are many different methods in the literature for registeringimages [Jan Modersitzki (2004), “Numerical Methods for ImageRegistration”, Oxford University Press, New York]. These methods areusually either feature or intensity based. Intensity based methods avoidthe feature extraction stage but work on whole intensity images so arecomputationally demanding. In the context of the DIET system as shown inFIGS. 1A and 1B, to capture the full motion of the breast surface withvery fine detail requires a large array of high resolution cameras andpotentially 1000's of frames for each camera to be analyzed to capturesmall subtle variations in the breast surface due to a small tumour.Thus methods that work with whole intensity images, for exampleNormalized Cross-Correlation (NCC) based [J. P. Lewis (1995), “FastNormalized Cross-Correlation”, Vision Interface, 120-1231 are notfeasible for the DIET system.

Feature based methods are potentially suitable since they minimize theamount of pixel information used. However natural feature extraction onthe breast may miss vital areas due to low contrast and consequentlymiss an abnormal surface perturbation due to a tumour. Thus the DIETsystem of the present invention preferably relies on tracking highdensities of artificially placed fiducial markers. In an alternativeembodiment, however, the fiducial markers are features of the tissueitself such as natural markers or changes in skin tone. This alternativewill require higher quality cameras (better contrast) and possibly moreimage processing, such as the use of color filters, than the preferredembodiment with artificial fiducial markers.

Common methods like snakes [M. Kass, A. Witkin and D. Terzopoiulos(1988), “Snakes: Active Contour Models”, International Journal ofComputer Vision, 1(4):321-331; N. Peterfreund (1999), “Robust trackingof position and velocity with Kalman Snakes”, IBEE Transactions onPattern Analysis and Machine Intelligence, 21(6):564-569] and gradientdecent (GDS) [Q. Qheng and R. Chellapa (1995), Automatic Feature PointExtraction and Tracking in Image Sequences for Arbitrary Camera Motion,International Journal of Computer vision, vol. 15, pp. 31-76] arelimited to small numbers of points, track each point separately and arehighly dependent on fuzziness or varying light conditions. It onlyrequires one occasion where a particular landmark point jumps to anotherlandmark nearby for the whole trajectory to be corrupted. There are alsomethods based on surface fitting of the points, for example thin platesplines [H. Chui and A. Rangarajan (2003), “A New Point MatchingAlgorithm for Non-Rigid Registration”, 89 (2-3);114-141]. However theproblem with these methods is the significantly large number of pointsthat need to be interpolated over as well as the huge number of timesthe methods would need to be applied to track tiny to large scaleoscillations of the breast surface, especially during non-steady statemotion. Thus the DIET system proposes unique challenges for imageregistration and motion tracking.

The present invention is generally shown in FIGS. 1A and 1B. FIG. 1Ashows the apparatus with the patient lying prone on a patient support105. A vibration unit 102 under the bed/table contacts a breast 108,which is preferably marked with artificial fiducial markers, asdescribed below. In an alternative embodiment, two or more vibrationunits 102 are used to actuate the breast from different locations. Anarray of cameras 104 below the bed/table captures images of the breast108 as it is vibrated by the vibration unit 102. The cameras 104 arepreferably high resolution cameras, such as those that produce 1mega-pixel frames. The vibration unit 102 vibrates the breast 108 at arate that is close to, but offset from the camera speed. For example,the vibration rate would be about 101 Hz for a camera speed of about 100frames per second. Of course, the camera speed may be a fraction of 100frames per second and used with the same vibration rate to achievesimilar results. The point is to capture a small amount of movement witheach frame. The low vibration rates (on the order of 100 Hz) are chosen(as opposed to ultrasonic rates typically used in elasto-tomography)because the breast tissue is much more responsive to vibrations nearthose frequencies than higher frequencies, such as ultrasonicfrequencies.

As shown in FIG. 1B, the cameras 104 are arrayed around the breast 108and calibrated such that any point on the breast is visible to at leasttwo cameras. The cameras 104 are spatially calibrated by standardtechniques used for tracking points with multiple cameras, and theimages captured by the cameras are transmitted to the computer 110 forimage registration and motion tracking with software to measure thesurface motion of the breast 108. The computer 110 then uses software toconvert the surface motion into a stiffness distribution 106.

Accordingly, the present invention focuses on pre-determined qualitiesand patterns in artificially placed fiducial markers and completelyreformulating the problem by computing a global motion invariantsignature. According to one aspect of the invention and referring toFIG. 2, markers are randomly applied to the breast according to a classof characteristics with two or more subclasses wherein each subclass hasa different number of markers. The class of characteristics may be, forexample, color wherein each subclass has markers with a common colorthat is different from the colors of markers in different classes. Forexample, one subclass may have red markers and another subclass may haveblue markers. In an alternative example, the class is shape and eachsubclass has markers with a common shape that is different from theshapes of markers in other subclasses. The different shapes may includea triangle, a circle, a square, etc. In another alternative example, theclass is size and each subclass has markers with a common size. Forsimplicity, the invention is described with the markers appliedaccording to a class having three primary colours with subclasses ofred, green and blue. In alternative embodiments, the markers may beapplied with a local pattern such as pattern having a red markersurrounded by four green markers and sixteen blue markers. Further, themarkers may use alternative colors to red, green, and blue.

In step 200 (FIG. 2), the coloured points red, green, and blue areplaced with increasing densities in the preferred ratio 1:4:16 where theblue points are in a sufficiently high density to accurately measuresurface motion on the scale of ≈1 cm². Alternative ratios may also beused, such as 1:2:4. The smaller number of red points serve to allowrapid overall motion tracking and the green and blue points account forprogressively smaller motions. The invention will accommodatesubstantially any density of markers; however, generally speaking, thehigher the density, the more accurate the results. A user may determinethe appropriate density of markers by considering the resolution of theimage and the desired accuracy of the analysis. For example, if onewanted an accuracy of 1 mm and a pixel covered a 0.01 mm² area, adensity of less than one marker per pixel would be sufficient. For anaccuracy of 1 μm with the same resolution, a density of more than 1-2dots per pixel would be required.

After identifying blue, green, and red points and a chosen sequence offrames representing breast motion in step 202, the global colour-basedsignatures representing the red points in each image are computed foreach pair of consecutive frames in step 204. This global signature isdeveloped using motion invariant properties of the markers, which do notchange substantially between the sequential frames. For example thedistances between a particular point and its two closest points isgenerally motion invariant. Step 204 is shown in more detail in Steps300 to 308 in FIG. 3 described below. In step 206, the global signaturefor the red points is used to interpolate between the red points toapproximately register first the green and then the blue points from thesecond frame to the first frame. The system performs a closest pointsearch to match the approximately registered green and blue points tothe respective green and blue points of the first frame.

The system then, using the data associated with the spatial calibrationof the cameras and cubic splines, computes the 3-D space curves for thered point trajectories parameterized in time for the chosen sequence offrames in step 208. In step 210, the system selects points on the redpoint 3-D continuous trajectories that correspond to the actual pointsin the chosen frame sequence to thereby use the computed curves topredict the location of the points in the respective frames. The systemcomputes the error between the predicted and actual motion in step 212using normalized cross-correlation (NCC) or another error metric. Ifthese computed errors are within tolerances set by the user, step 214outputs the final surface motion to a data file 216. Otherwise, step 214returns the system to step 202 to choose a new sequence of frames andrepeat the following steps. The loop continues until the error is withintolerances or it is stopped by the user.

These steps are then repeated for each camera to output the surfacemotion over the entire breast 108. This data may then be used todetermine the stiffness distribution 106 throughout the breast, such aswith Integral-Based Parameter Identification Applied tothree-dimensional Tissue Reconstruction in a DIET System described in mycopending patent application Integral-Based Parameter IdentificationApplied to Three-Dimensional tissue Stiffness Reconstruction in aDigital Image-Based Elasto-Tomography System, attorney docket number3023246 US01, the disclosure of which is herein incorporated byreference.

Referring to FIG. 3, the system forms the color based global signatureof the red points in each of two consecutive frames in the chosensequence by representing each red point by the distance to the closestgreen and blue point, respectively, in step 300. Thus the globalsignature is substantially invariant to local linear or affinetransformations. In step 302, the system locates the nearest red pointin the second frame to each of the red points in the first frame toprovide an initial correspondence of red points in the two frames. Thesystem in step 304 applies a maximum distance between the correlated redpoints set by a user and eliminates any correlated points that differ bydistances higher than this maximum. Step 304 thus eliminates a largenumber of non-corresponding points.

The system rules out further non-corresponding points in step 306 byforming a triangle between a red point, the closest point to the firstred point, and the second closest point to the first red point in thefirst frame and then comparing the lengths of the three sides to thesides of a triangle formed between the points in the second frame thatwere correlated to the three points in the first frame in step 302. Ifthe three differences between the three lengths is less than apredetermined tolerance, the two red points are accepted. If any of thedifferences in lengths are outside the tolerance, both points arerejected as non-corresponding. The remaining red points in the firstframe are each correlated to a point in the second frame so the systemmay compute and output a motion vector between the corresponding dots instep 308. Steps 300-308 are repeated for each set of two consecutiveframes in the chosen sequence.

Locally, with a high enough density of points, the motion over asufficiently small patch on the breast surface will be close to linear.Thus even though some patches will move significantly more than otherpatches on the breast surface, corresponding to significantly differentlocal linear or affine transformations, the majority of this globaldifference in motion will be corrected for by the signature. The problemof identifying landmark points between images related by a global motionreduces to the much simpler problem of identifying the overlap of twoglobal motion invariant signatures.

The differing densities of blue, green, and red points serves twopurposes. First, they maximise discrimination between non-correspondinglandmark points and second, they provide a hierarchical method ofmatching all the points, dramatically reducing the computation required.Referring to FIG. 2, the hierarchical method involves first matching thesmaller density of red points in step 204, then using interpolation anda closest point search to the higher density of green and blue points instep 206.

To specifically demonstrate one aspect of this invention, the rest ofthis detailed part of the disclosure will consist of three exampleswhich progressively become a more realistic representation of motiontracking in a DIET system. Consider an example where the global motionis a linear transformation:

(x,y)→(x cos θ+y sin θ+c, −x sin θ+y cos θ+f)  (1)

Let there be 40 blue dots, 20 green, and 10 red dots placed randomly ona 500K pixel image I; though, a ratio of 16:4:1 is preferred inpractice. A linear transformation of Equation (1) is applied on image Iwhere θ=45°, c=500, f=1000 to produce an image I. Random noise of up to±3 pixels is added to the points in I and Ī and 10% of each of the blue,green, and red dots are taken randomly out of the points on Ī tosimulate misidentification of colours. The blue dots in the tworesulting images are shown in the plot 400 of FIG. 4. In this case thelinear invariant signatures are calculated in step 300 for the blue dotsand shown in the linear invariant signatures plot 500 of FIG. 5. Forthis simple proof of concept example, the motion is very large, so steps302 and 306 are applied without step 304 to match the blue points andrule out non-corresponding points. The resulting corresponding bluepoints are used to compute the best least squares linear transformationmapping Ī to I. The resulting registration of Ī onto 1600 is shown inFIG. 6. All blue, green, and red points in the registered version of Īare then matched to the closest blue, green and red points in I within anoise threshold of 6 pixels. This process is repeated for 100 randomsimulations and no false identifications are made.

To demonstrate the concept on a non-uniform global motion field, 1000circles with a diameter of to pixels are randomly placed on a 1mega-pixel image I shown in the plot 700 of FIG. 7. 144 red points, 285green points, and 571 blue points are chosen to give a ratio of 1:2:4for the purpose of illustrating the invention, though the preferredratio is 1:4:16 in practice. The non-uniform motion field 800 shown inFIG. 8 is applied on the image I of FIG. 7 to produce a new image Ī.Using the centres of the circles, global signatures for the red pointsare calculated using step 300, and steps 302-306 are performed to matchthe red points. FIG. 9 shows a plot 900 illustrating step 306 with threered points, 902, 904, and 906, in I and three red points, 902′, 904′,and 906′, in Ī which are initially corresponding points after step 302and 304. Point 906/906′ is non-corresponding, however, and is denoted bya circle. This demonstrates the importance of Step 306 which wouldreject the circle of red points since the three distances of thetriangle in I are significantly different from the three distances ofthe corresponding triangle in Ī, with a maximum absolute difference of≈67 pixels, which is much greater than the error tolerance of 10 pixels.

Step 206 is then performed where interpolation is done withtwo-dimensional cubic splines which interpolate x-direction motion andy-direction motion between the red points separately. The plot 1000 ofFIG. 10 shows an example of blue points in Ī registered by interpolationbetween the red points onto blue points of I. After the registration aclosest point search matches all red, blue, and green points of image Īonto I.

Finally, to prove the concept on real images, two ≈1 mega-pixel images1100 and 1102 of two different deformations of a visco-elastic breastphantom with randomly placed coloured markers were taken, as shown inFIG. 11 though the colors are not shown in the black and white figures.Using a combination of thresholding and labeling connected 1 pixelpaths, the centre and colour of each marker are found. For simplicity,markers that overlap are automatically detected and are not used tobuild the signature. Applying steps 302-306 to determine the motion ofthe red points 204, then interpolating the red points usingtwo-dimensional cubic splines, to approximately register the green andblue points produces the registered images 1200 shown for the bluepoints in FIG. 12. The blue points corresponding to the image 1102 inFIG. 11 are denoted by crosses in FIG. 12 and show a close match. Aclosest point search is then performed to match the crosses precisely tothe corresponding points in the image 1100 in FIG. 11, which producesthe motion field 1300 shown in FIG. 13 for all the red, green, and bluepoints excluding overlaps.

The signature method can be applied on all consecutive pairs of framesin an actuation sequence to track a high density of points veryaccurately and with minimal computation. Furthermore the method is notrestricted to very small motion between images and so the method ofadaptive frame sampling in steps 202-214 can be applied, significantlyfurther reducing computation. Thus a global colour-based motioninvariant signature utilizing different proportions of fiducial markersin a hierarchical structure is a very effective method for trackinglarge numbers of points, ensuring that small local perturbations as wellas large global motions on the breast surface can be accurately measuredwith low computational requirements.

In an alternative embodiment, the markers may be applied in a globalpattern, such as in the case that the markers are applied to the breastwith a template. In a further alternative embodiment, the colors of themarkers may be ignored, or the markers may be applied in a single color.

The patient support 105 shown in FIG. 1A is illustrated as a bed/tablewith the patient in a prone position; however the DIET system 100 may bealternatively configured with the patient in a supine position. Further,the patient support 105 may be configured as a platform or a seat withthe patient in a non-horizontal position.

The invention has been described as tracking the surface motion of abreast for the purpose of providing data that may be used to determinethe stiffness distribution throughout the breast. However, the inventionmay alternatively be used to track the surface motion of any organ ortissue wherein dysfunction or damage may be determined by altered orvariable tissue elastic, damping, or mass properties. Further theinvention may be used in conjunction with MRI, laser sheets, pointlaser, PIV methods via digital or analog imaging, or ultrasound.

While the invention has been described with reference to particularembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the invention without departing from thescope of the invention.

PARTS LIST  100 DIET System  102 vibration unit  104 arrayed andcalibrated digital cameras  105 patient support  106 stiffnessdistribution of breast  108 breast  110 computer  200 step of placingpoints on breast  202 step of choosing sequence of frames andidentifying points  204 step of determining the global motion invariantsignature to track red points  206 step of interpolation to registerblue and green points  208 step of computing three-dimensionaltrajectories  210 step of predicting motion between frames  212 step ofcomparing predicted motion with actual motion  214 step of determiningif the results are within chosen tolerances  216 step of outputting thesurface motion  300 step of computing global signature using closestdistances  302 step of determining the initial correspondence of redpoints  304 step of performing the first test for ruling out points  306step of performing the second test for ruling out points  308 step ofoutputting the corresponding red points  400 first example plot showingblue dots in images I and I  500 linear invariant signatures plot  600registration of blue dots in I onto I  700 second example - binary imageplot  800 motion field  900 plot illustrating step 306  902, 904, redpoints in I and 906  902′, 904′, red points in I and 906′ 1000 plotregistering blue dots 1100 third example - visco-elastic breast phantomimage - first deformation 1102 visco-elastic breast phantom image -second deformation 1200 registered image 1300 motion field

1. A method for generating a high resolution feature registration andmotion tracking system with minimal computation wherein said methodcomprising the steps of: a) placing a plurality of fiducial markers on asurface of a breast, each of the markers having a characteristicaccording to a class of characteristics, the class having a plurality ofsubclasses wherein the markers in each subclass have a commoncharacteristic, the subclasses each having a unique number of markerswherein the unique number of markers in one subclass is different thanthe number of markers in any other subclass; b) tracking the motion ofthe markers of a first subclass having the fewest number of markers onthe surface from a first image to a second image; c) partitioning thesurface based upon the first subclass of markers; d) tracking the motionof the markers of a second subclass with the next fewest number ofmarkers within each partition; and c) partitioning the surface basedupon the second subclass of markers.
 2. The method of claim 1 whereinthe class of characteristics is colour.
 3. The method of claim 2 whereineach subclass has fiducial markers in a unique colour selected from thegroup consisting essentially of red, green, blue and black.
 4. Themethod of claim 2 wherein the fiducial markers are applied in glitterpaint.
 5. The method of claim 1 wherein the class of characteristics isshape and each subclass has markers with a common shape that is uniqueto the subclass.
 6. A method for generating a high resolution featureregistration and motion tracking system with minimal computation inconnection with a digital image-based elasto-tomography system, saidmethod comprising the steps of: (a) placing a plurality of fiducialmarkers on a tissue surface; (b) actuating the tissue surface; (c)imaging the tissue surface with an array of digital cameras; (d)choosing motion invariant properties of the fiducial markers to form aglobal motion invariant signature; (e) tracking the markers on theactuated tissue surface from image to image in each digital camera usingthe global motion invariant signature; and (d) using the tracked motionin each camera and the camera calibration to measure tissue surfacemotion.
 7. The method of claim 6, each of the fiducial markers having acharacteristic according to a class of characteristics, the class havinga plurality of subclasses wherein the markers in each subclass have acommon characteristic.
 8. The method of claim 7 wherein the motioninvariant properties of each subclass of fiducial markers is the closestdistances to fiducial markers of other subclasses.
 9. The method ofclaim 7 wherein the motion invariant properties of each subclass offiducial markers is the relative proportions of fiducial markers ofother subclasses within a predefined neighbourhood.
 10. The method ofclaim 9 further comprising the step of determining the three-dimensionalcoordinates of the fiducial markers according to a spatial calibrationof the cameras, wherein the predefined neighbourhood is all the markerswithin a chosen distance of each marker in a particular class asmeasured using the three-dimensional coordinates.
 11. The method ofclaim 7 wherein the motion invariant properties of each subclass offiducial markers is the closest distances to fiducial markers of othersubclasses and the proportions present of fiducial markers of othersubclasses within a predefined neighbourhood.
 12. The method of claim 7wherein the subclass of fiducial markers that are in the smallestproportion are used to form the global motion invariant signature.
 13. Amethod for generating a high resolution feature registration and motiontracking system with minimal computation in connection with a digitalimage-based elasto-tomography system said method comprising the stepsof: (a) placing a plurality of fiducial markers on a tissue surface; (b)imaging the tissue surface with an array of spatially calibrated digitalcameras; (c) choosing camera invariant properties of the fiducialmarkers and forming camera angle invariant signatures; (d) identifyingcommon markers in the images of a non-actuated tissue between allcameras in the array using the camera angle invariant signatures; (e)tracking the markers on the actuated tissue surface from image to imagein each digital camera using a global motion invariant signature; (f)using the tracked motion in each camera and the camera calibration tomeasure tissue surface motion.
 14. The method of claim 13 wherein thecamera angle invariant properties are affine invariant or projectiveinvariant.
 15. The method of claim 14 further comprising the steps ofclassifying the fiducial markers according to a chosen characteristicand grouping markings having common characteristics into subclasses;choosing one of the subclasses; determining the two or more closestpoints in non-chosen subclasses to each marker in the chosen subclass;and forming affine and projective invariant ratios of triangular areasbetween the markers.
 16. The method of claim 15 wherein the classes ofcharacteristics are selected from the group consisting essentially ofcolours and shapes.
 17. The method as claimed in claim 15 wherein classof characteristics is color and the fiducial markers comprise glitterpaint in colours selected from the group consisting essentially of red,blue, green and black.
 18. A digital image-based elasto-tomographyapparatus, comprising: an array of spatially calibrated cameras; avibration unit situated proximate to the array of cameras; and acomputer system in electrical communication with the cameras andconfigured for computing the surface motion of an object actuated by thevibration unit and within a field of view of the cameras.
 19. Thedigital image-based elasto-tomography apparatus of claim 18, wherein thecomputer computes the surface motion of the actuated object with aglobal motion invariant signature.
 20. The digital image-basedelasto-tomography apparatus of claim 18, further comprising a patientsupport proximate to the array of cameras and the vibration unit. 21.The digital image-based elasto-tomography apparatus of claim 20, thepatient support being a table.
 22. The digital image-basedelasto-tomography apparatus of claim 18, the object being artificiallymarked with a plurality of fiducial markers for the computer system totrack in computing the surface motion.